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Bonus-Malus Scale premiums for Tweedie’s compound Poisson models 特威迪复合泊松模型的奖金-马勒斯标度溢价率
IF 1.7
Annals of Actuarial Science Pub Date : 2024-05-21 DOI: 10.1017/s1748499524000113
Jean-Philippe Boucher, Raïssa Coulibaly
{"title":"Bonus-Malus Scale premiums for Tweedie’s compound Poisson models","authors":"Jean-Philippe Boucher, Raïssa Coulibaly","doi":"10.1017/s1748499524000113","DOIUrl":"https://doi.org/10.1017/s1748499524000113","url":null,"abstract":"Based on the recent papers, two distributions for the total claims amount (loss cost) are considered: compound Poisson-gamma and Tweedie. Each is used as an underlying distribution in the Bonus-Malus Scale (BMS) model. The BMS model links the premium of an insurance contract to a function of the insurance experience of the related policy. In other words, the idea is to model the increase and the decrease in premiums for insureds who do or do not file claims. We applied our approach to a sample of data from a major insurance company in Canada. Data fit and predictability were analyzed. We showed that the studied models are exciting alternatives to consider from a practical point of view, and that predictive ratemaking models can address some important practical considerations.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"142 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141147997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
DivFolio: a Shiny application for portfolio divestment in green finance wealth management DivFolio：绿色金融财富管理中投资组合撤资的闪亮应用程序
IF 1.7
Annals of Actuarial Science Pub Date : 2024-05-13 DOI: 10.1017/s1748499524000046
Pasin Marupanthorn, Gareth W. Peters, Eric D. Ofosu-Hene, Christina S. Nikitopoulos, Kylie-Anne Richards
{"title":"DivFolio: a Shiny application for portfolio divestment in green finance wealth management","authors":"Pasin Marupanthorn, Gareth W. Peters, Eric D. Ofosu-Hene, Christina S. Nikitopoulos, Kylie-Anne Richards","doi":"10.1017/s1748499524000046","DOIUrl":"https://doi.org/10.1017/s1748499524000046","url":null,"abstract":"This paper introduces <jats:italic>DivFolio</jats:italic>, a multiperiod portfolio selection and analytic software application that incorporates automated and user-determined divestment practices accommodating Environmental Social Governance (ESG) and portfolio carbon footprint considerations. This freely available portfolio analytics software tool is written in R with a GUI interface developed as an R Shiny application for ease of user experience. Users can utilize this software to dynamically assess the performance of asset selections from global equity, exchange-traded funds, exchange-traded notes, and depositary receipts markets over multiple time periods. This assessment is based on the impact of ESG investment and fossil-fuel divestment practices on portfolio behavior in terms of risk, return, stability, diversification, and climate mitigation credentials of associated investment decisions. We highlight two applications of <jats:italic>DivFolio</jats:italic>. The first revolves around using sector scanning to divest from a specialized portfolio featuring constituents of the FTSE 100. The second, rooted in actuarial considerations, focuses on divestment strategies informed by environmental risk assessments for mixed pension portfolios in the US and UK.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"129 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140930888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Valuation of guaranteed minimum accumulation benefits (GMABs) with physics-inspired neural networks 利用物理启发神经网络评估最低保证累积福利（GMABs）
IF 1.7
Annals of Actuarial Science Pub Date : 2024-05-13 DOI: 10.1017/s1748499524000095
Donatien Hainaut
{"title":"Valuation of guaranteed minimum accumulation benefits (GMABs) with physics-inspired neural networks","authors":"Donatien Hainaut","doi":"10.1017/s1748499524000095","DOIUrl":"https://doi.org/10.1017/s1748499524000095","url":null,"abstract":"Guaranteed minimum accumulation benefits (GMABs) are retirement savings vehicles that protect the policyholder against downside market risk. This article proposes a valuation method for these contracts based on physics-inspired neural networks (PINNs), in the presence of multiple financial and biometric risk factors. A PINN integrates principles from physics into its learning process to enhance its efficiency in solving complex problems. In this article, the driving principle is the Feynman–Kac (FK) equation, which is a partial differential equation (PDE) governing the GMAB price in an arbitrage-free market. In our context, the FK PDE depends on multiple variables and is difficult to solve using classical finite difference approximations. In comparison, PINNs constitute an efficient alternative that can evaluate GMABs with various specifications without the need for retraining. To illustrate this, we consider a market with four risk factors. We first derive a closed-form expression for the GMAB that serves as a benchmark for the PINN. Next, we propose a scaled version of the FK equation that we solve using a PINN. Pricing errors are analyzed in a numerical illustration.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"43 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140930680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
De-risking in multi-state life and health insurance 多州人寿和健康保险的去风险化
IF 1.7
Annals of Actuarial Science Pub Date : 2024-04-22 DOI: 10.1017/s1748499524000083
Susanna Levantesi, Massimiliano Menzietti, Anna Kamille Nyegaard
{"title":"De-risking in multi-state life and health insurance","authors":"Susanna Levantesi, Massimiliano Menzietti, Anna Kamille Nyegaard","doi":"10.1017/s1748499524000083","DOIUrl":"https://doi.org/10.1017/s1748499524000083","url":null,"abstract":"The calculation of life and health insurance liabilities is based on assumptions about mortality and disability rates, and insurance companies face systematic insurance risks if assumptions about these rates change. In this paper, we study how to manage systematic insurance risks in a multi-state setup by considering securities linked to the transition intensities of the model. We assume there exists a market for trading two securities linked to, for instance, mortality and disability rates, the de-risking option and the de-risking swap, and we describe the optimization problem to find the de-risking strategy that minimizes systematic insurance risks in a multi-state setup. We develop a numerical example based on the disability model, and the results imply that systematic insurance risks significantly decrease when implementing de-risking strategies.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"26 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140635614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Smoothness and monotonicity constraints for neural networks using ICEnet 利用 ICEnet 实现神经网络的平滑性和单调性约束
IF 1.7
Annals of Actuarial Science Pub Date : 2024-04-01 DOI: 10.1017/s174849952400006x
Ronald Richman, Mario V. Wüthrich
{"title":"Smoothness and monotonicity constraints for neural networks using ICEnet","authors":"Ronald Richman, Mario V. Wüthrich","doi":"10.1017/s174849952400006x","DOIUrl":"https://doi.org/10.1017/s174849952400006x","url":null,"abstract":"<p>Deep neural networks have become an important tool for use in actuarial tasks, due to the significant gains in accuracy provided by these techniques compared to traditional methods, but also due to the close connection of these models to the generalized linear models (GLMs) currently used in industry. Although constraining GLM parameters relating to insurance risk factors to be smooth or exhibit monotonicity is trivial, methods to incorporate such constraints into deep neural networks have not yet been developed. This is a barrier for the adoption of neural networks in insurance practice since actuaries often impose these constraints for commercial or statistical reasons. In this work, we present a novel method for enforcing constraints within deep neural network models, and we show how these models can be trained. Moreover, we provide example applications using real-world datasets. We call our proposed method <span>ICEnet</span> to emphasize the close link of our proposal to the individual conditional expectation model interpretability technique.</p>","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"152 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Interpretable zero-inflated neural network models for predicting admission counts 用于预测入院人数的可解释零膨胀神经网络模型
IF 1.7
Annals of Actuarial Science Pub Date : 2024-03-26 DOI: 10.1017/s1748499524000058
Alex Jose, Angus S. Macdonald, George Tzougas, George Streftaris
{"title":"Interpretable zero-inflated neural network models for predicting admission counts","authors":"Alex Jose, Angus S. Macdonald, George Tzougas, George Streftaris","doi":"10.1017/s1748499524000058","DOIUrl":"https://doi.org/10.1017/s1748499524000058","url":null,"abstract":"<p>In this paper, we construct interpretable zero-inflated neural network models for modeling hospital admission counts related to respiratory diseases among a health-insured population and their dependants in the United States. In particular, we exemplify our approach by considering the zero-inflated Poisson neural network (ZIPNN), and we follow the combined actuarial neural network (CANN) approach for developing zero-inflated combined actuarial neural network (ZIPCANN) models for modeling admission rates, which can accommodate the excess zero nature of admission counts data. Furthermore, we adopt the LocalGLMnet approach (Richman &amp; Wüthrich (2023). <span>Scandinavian Actuarial Journal</span>, 2023(1), 71–95.) for interpreting the ZIPNN model results. This facilitates the analysis of the impact of a number of socio-demographic factors on the admission rates related to respiratory disease while benefiting from an improved predictive performance. The real-life utility of the methodologies developed as part of this work lies in the fact that they facilitate accurate rate setting, in addition to offering the potential to inform health interventions.</p>","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"234 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140301621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
GEMAct: a Python package for non-life (re)insurance modeling GEMAct：用于非寿险（再）保险建模的 Python 软件包
IF 1.7
Annals of Actuarial Science Pub Date : 2024-02-14 DOI: 10.1017/s1748499524000022
Gabriele Pittarello, Edoardo Luini, Manfred Marvin Marchione
{"title":"GEMAct: a Python package for non-life (re)insurance modeling","authors":"Gabriele Pittarello, Edoardo Luini, Manfred Marvin Marchione","doi":"10.1017/s1748499524000022","DOIUrl":"https://doi.org/10.1017/s1748499524000022","url":null,"abstract":"This paper introduces gemact, a Python package for actuarial modeling based on the collective risk model. The library supports applications to risk costing and risk transfer, loss aggregation, and loss reserving. We add new probability distributions to those available in scipy, including the (a, b, 0) and (a, b, 1) discrete distributions, copulas of the Archimedean family, the Gaussian, the Student t and the Fundamental copulas. We provide an implementation of the AEP algorithm for calculating the cumulative distribution function of the sum of dependent, nonnegative random variables, given their dependency structure specified with a copula. The theoretical framework is introduced at the beginning of each section to give the reader with a sufficient understanding of the underlying actuarial models.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"221 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139757709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The discrete-time arbitrage-free Nelson-Siegel model: a closed-form solution and applications to mixed funds representation 离散时间无套利的 Nelson-Siegel 模型：封闭式解法及混合基金表示法的应用
IF 1.7
Annals of Actuarial Science Pub Date : 2024-02-12 DOI: 10.1017/s1748499524000010
Ramin Eghbalzadeh, Frédéric Godin, Patrice Gaillardetz
{"title":"The discrete-time arbitrage-free Nelson-Siegel model: a closed-form solution and applications to mixed funds representation","authors":"Ramin Eghbalzadeh, Frédéric Godin, Patrice Gaillardetz","doi":"10.1017/s1748499524000010","DOIUrl":"https://doi.org/10.1017/s1748499524000010","url":null,"abstract":"A closed-form solution for zero-coupon bonds is obtained for a version of the discrete-time arbitrage-free Nelson-Siegel model. An estimation procedure relying on a Kalman filter is provided. The model is shown to produce adequate fit when applied to historical Canadian spot rate data and to improve distributional predictive performance over benchmarks. An adaptation of the mixed fund return model from Augustyniak <jats:italic>et al</jats:italic>. ((2021). <jats:italic>ASTIN Bulletin: The Journal of the IAA</jats:italic>, <jats:italic>51</jats:italic>(1), 131–159.) is also provided to include the discrete-time arbitrage-free Nelson-Siegel model as one of its building blocks.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"324 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139757858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
On clustering levels of a hierarchical categorical risk factor 关于分层分类风险因素的聚类水平
IF 1.7
Annals of Actuarial Science Pub Date : 2024-02-01 DOI: 10.1017/s1748499523000283
Bavo D.C. Campo, Katrien Antonio
{"title":"On clustering levels of a hierarchical categorical risk factor","authors":"Bavo D.C. Campo, Katrien Antonio","doi":"10.1017/s1748499523000283","DOIUrl":"https://doi.org/10.1017/s1748499523000283","url":null,"abstract":"<p>Handling nominal covariates with a large number of categories is challenging for both statistical and machine learning techniques. This problem is further exacerbated when the nominal variable has a hierarchical structure. We commonly rely on methods such as the random effects approach to incorporate these covariates in a predictive model. Nonetheless, in certain situations, even the random effects approach may encounter estimation problems. We propose the data-driven Partitioning Hierarchical Risk-factors Adaptive Top-down algorithm to reduce the hierarchically structured risk factor to its essence, by grouping similar categories at each level of the hierarchy. We work top-down and engineer several features to characterize the profile of the categories at a specific level in the hierarchy. In our workers’ compensation case study, we characterize the risk profile of an industry via its observed damage rates and claim frequencies. In addition, we use embeddings to encode the textual description of the economic activity of the insured company. These features are then used as input in a clustering algorithm to group similar categories. Our method substantially reduces the number of categories and results in a grouping that is generalizable to out-of-sample data. Moreover, we obtain a better differentiation between high-risk and low-risk companies.</p>","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"120 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139657706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Epidemic modelling and actuarial applications for pandemic insurance: a case study of Victoria, Australia 大流行病保险的流行病建模和精算应用：澳大利亚维多利亚州的案例研究
IF 1.7
Annals of Actuarial Science Pub Date : 2024-01-09 DOI: 10.1017/s1748499523000246
Chang Zhai, Ping Chen, Zhuo Jin, Tak Kuen Siu
{"title":"Epidemic modelling and actuarial applications for pandemic insurance: a case study of Victoria, Australia","authors":"Chang Zhai, Ping Chen, Zhuo Jin, Tak Kuen Siu","doi":"10.1017/s1748499523000246","DOIUrl":"https://doi.org/10.1017/s1748499523000246","url":null,"abstract":"With the recent outbreak of COVID-19, evaluating the epidemic risk appears to be a pressing issue of global concern and one of the major challenges recently. In the fight against pandemics, the ability to understand, model, and forecast the transmission dynamics of infectious diseases plays a crucial role. This paper provides an overview of foundational compartment models and introduces the Susceptible-Exposed-Infected-Containing-3-Substates-Recovered-Dead model to study the dynamics of COVID-19. A meticulous data calibration procedure is employed to study the evolution trend of an actual pandemic using real-world data from Victoria, Australia. Additionally, the paper discusses innovative applications of epidemic models to the insurance industry, which are currently under investigation. Through the use of the newly developed analytically tractable model, insurance companies are able to determine fair premium levels during an outbreak. Moreover, the paper provides practical guidance for insurance companies by examining the variation in reserve levels over time.","PeriodicalId":44135,"journal":{"name":"Annals of Actuarial Science","volume":"34 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139411981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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